Quick yield enhancement is one of the critical aspects of engineering chain management. In the sub-wavelength era, cycle time requirement becomes more stringent because capital investment is sky rocketing while market demands change more rapidly and the manufacturing process, equipments and operations become more complicated than before. Fast yield ramping is founded on effective management of knowledge intensive yield analysis. In semiconductor manufacturing, wafer-acceptance-test (WAT) data is monitored to detect if there are electrical performance deviations and then to diagnose the root causes of manufacturing processes. To store and retrieve WAT data for yield analysis, engineers perform WAT data naming, engineers have individual naming rules under the guideline to do WAT naming and the naming rule knowledge is often kept to individual engineers. As a result, two types of misalignment of WAT data naming may occur: one same WAT data item with different names or a WAT data item name representing several WAT data items. In this thesis, we investigate legacy WAT naming alignment problem and focus on design of automatic WAT naming alignment among legacy WAT data names to facilitate effective sharing and reuse in yield analysis. Specific design challenges are as follows: C1) How to extract and represent knowledge of WAT data item naming and C2) How to automatically align WAT legacy names to golden names without requiring extra effort Two mechanisms are designed to conquer these challenges respectively as follow: 1. Establishment of ontology-based WAT data item naming knowledge model (WATDIKM): Ontology-based knowledge representation mechanism, we adopt first order logic (FOL) approach because of good logical expression, and transfer FOL into semantic network (SN) modeling approach for the purpose of easier comprehension by engineer. Semantic network consist of node types, nodes and node links. In the context of WAT data item naming, nodes correspond to concepts like WAT measure item, etc, while the nodes which have the same property are classified into a node type which include “Device Concept”, “WAT Concept”, “Test Line Concept” and “Test Method Concept.” Node link corresponds to the relation between two nodes. Node link contain “Inherit”, “Measure”, “Input” and “Output.” The knowledge of WAT data item naming are represented by WAT data item naming knowledge model (WATDIKM). 2. Establishment of automation alignment mechanism: This mechanism includes WAT data item name description algorithm (WATDIND) and WAT data item naming alignment algorithm (WATDINA). WATDIND algorithm can extract node value of WAT data item from existing WAT data naming by node links of WATDNKM. And node values are stored in ontological WAT data item name description table. Based-on WATDNKM, WATDINNA algorithm constructs WAT data item name descriptions for the two data names. Afterward, compare the two data name description. If data name descriptions are the same, then they can be aligned. If the two WAT data items have the same name and different name descriptions, they are not the same data item. To reveal the knowledge extraction mechanisms which are achievable, the three new designs have been implemented as module and integrated into a Service Oriented Architecture (SOA) based EDA platform. The three main values of the platform are enhanced by the mechanisms as follow: 1. Knowledge of WAT data item naming can be extracted from existing data by constructing the ontology–based WAT data item naming knowledge model (WATDINKM). 2. facilitates significant reducing in name mapping table constructing effort by change pair-wise alignment method to ontology-based alignment method 3. Provide fast and accurate WAT naming alignment to facilitate quick analysis WAT data by analysis tools and foundation of establishing golden names.